Browsing by Author "Jiang, Zhehan"
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Item Diagnostic Classification Models for Ordinal Item Responses(Frontiers, 2018) Liu, Ren; Jiang, Zhehan; University of California Merced; University of Alabama TuscaloosaThe purpose of this study is to develop and evaluate two diagnostic classification models (DCMs) for scoring ordinal item data. We first applied the proposed models to an operational dataset and compared their performance to an epitome of current polytomous DCMs in which the ordered data structure is ignored. Findings suggest that the much more parsimonious models that we proposed performed similarly to the current polytomous DCMs and offered useful item-level information in addition to option-level information. We then performed a small simulation study using the applied study condition and demonstrated that the proposed models can provide unbiased parameter estimates and correctly classify individuals. In practice, the proposed models can accommodate much smaller sample sizes than current polytomous DCMs and thus prove useful in many small-scale testing scenarios.Item Estimating Cognitive Diagnosis Models in Small Samples: Bayes Modal Estimation and Monotonic Constraints(Sage, 2021) Ma, Wenchao; Jiang, Zhehan; University of Alabama Tuscaloosa; Peking UniversityDespite the increasing popularity, cognitive diagnosis models have been criticized for limited utility for small samples. In this study, the authors proposed to use Bayes modal (BM) estimation and monotonic constraints to stabilize item parameter estimation and facilitate person classification in small samples based on the generalized deterministic input noisy "and" gate (G-DINA) model. Both simulation study and real data analysis were used to assess the utility of the BM estimation and monotonic constraints. Results showed that in small samples, (a) the G-DINA model with BM estimation is more likely to converge successfully, (b) when prior distributions are specified reasonably, and monotonicity is not violated, the BM estimation with monotonicity tends to produce more stable item parameter estimates and more accurate person classification, and (c) the G-DINA model using the BM estimation with monotonicity is less likely to overfit the data and shows higher predictive power.Item Fitting Large Factor Analysis Models With Ordinal Data(Sage, 2019) DiStefano, Christine; McDaniel, Heather L.; Zhang, Liyun; Shi, Dexin; Jiang, Zhehan; University of South Carolina Columbia; University of Alabama TuscaloosaA simulation study was conducted to investigate the model size effect when confirmatory factor analysis (CFA) models include many ordinal items. CFA models including between 15 and 120 ordinal items were analyzed with mean- and variance-adjusted weighted least squares to determine how varying sample size, number of ordered categories, and misspecification affect parameter estimates, standard errors of parameter estimates, and selected fit indices. As the number of items increased, the number of admissible solutions and accuracy of parameter estimates improved, even when models were misspecified. Also, standard errors of parameter estimates were closer to empirical standard deviation values as the number of items increased. When evaluating goodness-of-fit for ordinal CFA with many observed indicators, researchers should be cautious in interpreting the root mean square error of approximation, as this value appeared overly optimistic under misspecified conditions.Item Improving generalizability coefficient estimate accuracy: A way to incorporate auxiliary informationJiang, Zhehan; Walker, Kevin W.; Shi, Dexin; Cao, Jian; University of Alabama TuscaloosaItem Indices of Subscore Utility for Individuals and Subgroups Based on Multivariate Generalizability Theory(Sage, 2020) Raymond, Mark R.; Jiang, Zhehan; University of Alabama TuscaloosaConventional methods for evaluating the utility of subscores rely on traditional indices of reliability and on correlations among subscores. One limitation of correlational methods is that they do not explicitly consider variation in subtest means. An exception is an index of score profile reliability designated as G, which quantifies the ratio of true score profile variance to observed score profile variance. G has been shown to be more sensitive than correlational methods to group differences in score profile utility. However, it is a group average, representing the expected value over a population of examinees. Just as score reliability varies across individuals and subgroups, one can expect that the reliability of score profiles will vary across examinees. This article proposes two conditional indices of score profile utility grounded in multivariate generalizability theory. The first is based on the ratio of observed profile variance to the profile variance that can be attributed to random error. The second quantifies the proportion of observed variability in a score profile that can be attributed to true score profile variance. The article describes the indices, illustrates their use with two empirical examples, and evaluates their properties with simulated data. The results suggest that the proposed estimators of profile error variance are consistent with the known error in simulated score profiles and that they provide information beyond that provided by traditional measures of subscore utility. The simulation study suggests that artificially large values of the indices could occur for about 5% to 8% of examinees. The article concludes by suggesting possible applications of the indices and discusses avenues for further research.Item Integrating Differential Evolution Optimization to Cognitive Diagnostic Model Estimation(Frontiers, 2018) Jiang, Zhehan; Ma, Wenchao; University of Alabama TuscaloosaA log-linear cognitive diagnostic model (LCDM) is estimated via a global optimization approach- differential evolution optimization (DEoptim), which can be used when the traditional expectation maximization (EM) fails. The application of the DEoptim to LCDM estimation is introduced, explicated, and evaluated via a Monte Carlo simulation study in this article. The aim of this study is to fill the gap between the field of psychometric modeling and modern machine learning estimation techniques and provide an alternative solution in the model estimation.Item Modeling time-to-trigger in library demand-driven acquisitions via survival analysis(Elsevier, 2019-07) Jiang, Zhehan; Fitzgerald, Sarah Rose; Walker, Kevin W.; University of Alabama TuscaloosaConventional statistical methods (e.g. logistics regression, decision tree, etc.) have been used to analyze library demand-driven acquisitions (DDA) data. However, these methods are not well-suited to predict when acquisitions will be triggered or how long c-books will remain unused. Survival analysis, a statistical method commonly used in clinical research and medical trials, was employed to predict the time-to-trigger for DDA purchases within the context of a large research university library. By predicting which e-books will be triggered (i.e., purchased), as well as the time to trigger occurrence, the method tested in this study provides libraries a deeper understanding of factors influencing their DDA purchasing patterns. This understanding will help libraries optimize their DDA profile management and DDA budgets. This research provides a demonstration of how data science techniques can be of value for the library environment.Item Promoting Institutional Repositories via Visualizations: A Changepoint Study(2019-03-11) Jiang, Zhehan; Fitzgerald, Sarah Rose; University of Alabama TuscaloosaThis article examines whether implementing visualizations on an institutional repository webpage increases traffic on the site. Two methods for creating visualizations to attract faculty and student interest were employed. The first is a map displaying usage of institutional repository content from around the world. This map uses Tableau software to display Google Analytics data. The second method is a text mining tool allowing users to generate word clouds from dissertation and thesis abstracts according to discipline and year of publication. The word cloud uses R programing language, the Shiny software package, and a text mining package called tm. Change in the number of institutional repository website sessions was analyzed through change-point analysis.Item The Use of Multivariate Generalizability Theory to Evaluate the Quality of Subscores(Sage, 2018) Jiang, Zhehan; Raymond, Mark; University of Alabama TuscaloosaConventional methods for evaluating the utility of subscores rely on reliability and correlation coefficients. However, correlations can overlook a notable source of variability: variation in subtest means/difficulties. Brennan introduced a reliability index for score profiles based on multivariate generalizability theory, designated as G, which is sensitive to variation in subtest difficulty. However, there has been little, if any, research evaluating the properties of this index. A series of simulation experiments, as well as analyses of real data, were conducted to investigate G under various conditions of subtest reliability, subtest correlations, and variability in subtest means. Three pilot studies evaluated G in the context of a single group of examinees. Results of the pilots indicated that G indices were typically low; across the 108 experimental conditions, G ranged from .23 to .86, with an overall mean of 0.63. The findings were consistent with previous research, indicating that subscores often do not have interpretive value. Importantly, there were many conditions for which the correlation-based method known as proportion reduction in mean-square error (PRMSE; Haberman, 2006) indicated that subscores were worth reporting, but for which values of G fell into the .50s, .60s, and .70s. The main study investigated G within the context of score profiles for examinee subgroups. Again, not only G indices were generally low, but it was also found that G can be sensitive to subgroup differences when PRMSE is not. Analyses of real data and subsequent discussion address how G can supplement PRMSE for characterizing the quality of subscores.